Rbc
Lead Agentic AI Engineer
Minneapolis, Minnesota, United States of America · Full-time
Sponsorship not specified$100k-$170kDetected 5 days ago
PythonFastAPIExpressMachine LearningData ScienceNLPLLMsRAGAgentic AIAI OrchestrationA/B TestingSalesforceCRMLeadershipCollaborationMentoringActuarial Science
About the role
- This is a high-ambiguity, rapid experimentation role where you'll define how vendor agents, enterprise frameworks, and internally developed agents coexist and interoperate within a governed ecosystem.
- We care about each other, reaching our potential, making a difference to our communities, and achieving success that is mutual.
Responsibilities
- We thrive on the challenge to be our best, progressive thinking to keep growing, and working together to deliver trusted advice to help our clients thrive and communities prosper.
- Leaders who support your development through coaching and managing opportunities.
- Opportunities to build close relationships with clients.
- RBC strives to deliver this through policies and programs intended to foster a workplace based on respect, belonging and opportunity for all.
- Expand your limits and create a new future together at RBC.
- Find out how we use our passion and drive to enhance the well-being of our clients and communities at jobs.rbc.com.
- Learn more at rbc.com. We are proud to support a broad range of community initiatives through donations, community investments and employee volunteer activities.
Skills
- Job Description What is the opportunity?
- 250 NICOLLET MALL:MINNEAPOLIS
Compensation
- The expected salary range for this particular position is $100,000 - $170,000, depending on your experience, skills, and registration status, market conditions and business needs.
Benefits
- Our success comes from the 84,000+ employees who bring our vision, values and strategy to life so we can help our clients thrive and communities prosper.
Company info
- Ability to make a difference and lasting impact.
This listing is sourced directly from Rbc's careers page and normalized into a canonical job model.